U-net Ensemble Model for Segmentation inHistopathology Images

Yilong Li, Xingru Huang, Yaqi Wang, Zhaoyang Xu, Yibao Sun, and Qianni Zhang

Abstract: In this work, a multi-scale U-net fusion model is proposed for the automatic cancer detection and classification in whole-slide lung histopathology. The model integrates two types of U-net structure, trained on different image scales and subsets, aiming to address the challenges posed by the significant variation in data presentation. Since lung histopathology images come in various sub-categories and appearances, the performance of an individual trained network is usually limited. We train a variety of networks by using multiple re-scaled images and different subsets of images, and finally ensemble the outputs of various networks. Smoothing and noise elimination are conducted using convolutional Conditional Random Fields (CRFs). The proposed model isvalidated on Automatic Cancer Detection and Classification in Whole-slide Lung Histopathology (ACDC@LungHP) challenge in ISBI2019. Our method achieves a dice coefficient of 0.7968, Which is ranked at the third place on the board.

Keywords: Model ensemble, Tumor, Segmentation, Convolutional CRFs